Education
PhD, Tsinghua University, Nuclear Technology and Applications, 2021
Bachelor, Xian Jiaotong University, Nuclear Science and Technology, 2015
Working experiences
01/2026-present, Research Associate Professor, Division of Applied Nuclear Technology, INET
06/2023-01/2026, Research Assistant Professor, Division of Applied Nuclear Technology, INET
04/2021-06/2026, Postdoc, Division of Applied Nuclear Technology, INET
Areas of Research Interests
Radiation imaging in nuclear technology applications, including digital radiation image processing, CT image reconstruction algorithms, and deep learning techniques.
Research Status
Digital Radiation Image Restoration Algorithms:Employ deep learning techniques such as convolutional neural networks and diffusion models to restore degraded images and improve the imaging quality of digital radiation systems.
CT Reconstruction Algorithms:Propose unsupervised reconstruction algorithms for incomplete and low-dose CT data to enhance image reconstruction quality.
Radiation Imaging Simulation:Simulate the interaction of X/γ-rays with matter using the Monte Carlo algorithm, with parallel acceleration implemented on GPU.
Selected Academic Achievement
[1] Chang, Jiahao, et al. "An unsupervised sparse‐view CT reconstruction framework using combination of iterative deep image prior and ADMM." Medical Physics 52.7 (2025): e17933.
[2] Fu, J., Cong, P., Xu, S., Chang, J., Liu, X., & Sun, Y*. (2025). Neural architecture search with Deep Radon Prior for sparse‐view CT image reconstruction. Medical Physics, 52(5), 3044-3058.
[3] Chang, J., Tang, P., Jiang, Z., Wang, Z., Wu, Z., & Sun, Y*. (2025). Unsupervised Deblurring Algorithm Based on Deep Image Prior for Vehicle Detection Systems. IEEE Transactions on Nuclear Science.
[4] Liu, R., Sun, Y., Liu, X., & Cong, P. (2023). Enhanced data augmentation for denoising and super-resolution reconstruction of radiation images. IEEE Transactions on Nuclear Science, 70(9), 2183-2190.
[5] Zhao, Zhongwei, Yuewen Sun, and Peng Cong. "Sparse-view CT reconstruction via generative adversarial networks." 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). IEEE, 2018.
